Exposure signature of metal oxide nanoparticles PIs: Christine Payne, Ph.D., Yuhong Fan, Ph.D., & Melissa Kemp, Ph.D. Metal oxide nanoparticles, used in manufacturing and consumer products , are widely recognized to cause cellular oxidation. Oxidation is often used as a measure of cytotoxicity in the health risk assessment of nanomaterials. However, the role of nanoparticles in disrupting epigenetic regulation is largely uncharacterized, in large part due to the complex involvement of subcellular redox compartmentation, protein thiol regulation, and metabolic co-factors. Our long-term goal is to provide prediction of toxicological risks and safety based upon the ?memory? of an acute event of nanoparticle-induced stress. We hypothesize that H2O2 gradients induced by metal oxide nanoparticles will result in redox-triggered signaling and epigenetic events that control subsequent gene expression. Our objective is to establish how features of the complex cellular oxidative landscape impact nuclear processes in order to build predictive models of exposure oxidation and epigenetic outcomes. We will accomplish this objective by i) characterizing intracellular H2O2 dynamics during nanoparticle binding and uptake through a novel microtubule-bound ratiometric reporter; ii) identifying epigenetic signatures associated with non-lethal exposures to metal oxide nanoparticles; and iii) developing statistical models of metal-oxide nanoparticle-induced control over epigenetic regulation through the use of image-derived metrics. Throughout the project, innovations in live cell microscopy and integrative data analysis will allow us to test our predictions on the sustained impact of nanoparticle exposure inherited by progeny cells. The outcomes of this project will be an unprecedented, quantitative characterization of the long-term oxidative effects induced by metal oxide nanoparticles, the extraction of new hypotheses of oxidative epigenetic regulatory mechanisms that lead to cellular memory of sub-lethal, toxicological exposure, and will be directly applicable to high-content toxicological screens for the prediction of lasting effects induced by oxidative stress.